The project team will work on extracting key concepts from scholarly publications and explore techniques for building a taxonomy of extracted concepts by leveraging open knowledge bases (e.g., Wikipedia). The outcome of this process will be evaluated for various science and technology knowledge platform-based analysis services. The techniques, which reduce semantic ambiguity, will analyze…

The Midwest Big Data Hub (MBDH) is a network of regional institutions created to facilitate collection, management, and use of complex information, creating active partnerships of experts and resources to address issues relevant to life in the Midwest.

MBDH aims to help member organizations working in Big Data coordinate…

Catastrophic events such as Fukushima and Katrina have made it clear that integrating physical and social causes of failure into a cohesive modeling framework is critical in order to prevent complex technological accidents and to maintain public safety and health. In this research, experts in Probabilistic Risk Assessment (PRA), Organizational Behavior and Information Science and Data…

Nowadays, as an intrinsic property of big data, data heterogeneity can be seen in a variety of real world applications, ranging from security to manufacturing, from healthcare to crowdsourcing. Many high-impact data mining applications exhibit the co-existence of multiple types of heterogeneity, such as different classification tasks, different data sources, and different labeling oracles.…

Changes of a galaxy's properties over time are driven by the quantity of its cold gas, the raw material from which stars form. Thus, understanding the properties of a galaxy's cold gas component will tell us both how the star formation process changes over time and how this affects galaxies. Using a recently completed survey of carbon monoxide gas in a sample of nearby galaxies, the project…

The scientific challenge for this project is to accelerate discovery and exploration of the synthetic biology design space. In particular, many parts used in synthetic biology come from or are initially tested in a simple bacteria, E. coli, but many potential applications in energy, agriculture, materials, and health require either different bacteria or higher level organisms (yeast for…

Scientific discovery across the physical sciences is increasingly dependent on the analysis of volumetric - or three-dimensional - data, that may come from a supercomputer simulation, direct measurement, or mathematical models. Researchers typically seek to extract meaningful insights from this data by visualizing and analyzing it in various ways. The ways in which scientists process…

The overall goal of this project is to advance knowledge of the long-term impacts of international funding for biodiversity conservation from private foundations and the factors associated with sustainable conservation gains over time. The project will analyze funding from private foundations, with a focus on MacArthur Foundation investment in conservation as a case study. Our team will focus…

The University of Illinois, in collaboration with multiple community college and public libraries, will develop a data storytelling toolkit for libraries (DSTL) to support meaningful and effective data communication, empowering libraries to utilize the rapidly growing data landscape. DSTL will connect real-world examples of data use with data stories (including narrative strategies and data…

This project will study diabetes dedicated social networks. It aims to harness diabetes patients' online social behaviors from multiple networks to predict their biomarker measurements such as glycated hemoglobin and fasting blood glucose. This project will provide a paradigm shift from exploration to prediction compared with state-of-the-art research on diabetes dedicated social networks,…

How do limitations and intransparencies in data quality and data provenance bias research outcomes, and how can we detect and mitigate these limitations? For example, we have been investigating the impact of entity resolution errors on network analysis results. We found that commonly reported network metrics and derived implications can strongly deviate from the truth—as established based on…

It is difficult to understand the effectiveness of various treatment options when a huge number of external factors such as lifestyle, diet, and environment affect the burden of a disease. A major barrier to understanding is the challenge of scale—sampling enough patients to separate the major, minor, and negligible factors. With access to a database of more than one trillion public social…

INDICATOR is a novel information system for collecting, integrating, and analyzing data from multiple sources to provide public health decision makers real-time data on the health of their community. Data comes from sources as varied as emergency department visits, school attendance, veterinary clinics, and social media postings and together have been used to change public policy in outbreak…

The U.S. scientific workforce is aging - the average age of both US academics and medical school faculty increased to the late 40s, from the early 40s in 1970. This aging is troubling because people are seen to make important scientific contributions early in their careers. Moreover, the U.S. is turning to innovation as an economic driver, and the aging of the population will both increase and…

Copyright law and resource licensing complicate the application of text data mining for research. This project convened a National Forum on Text Data Mining with Use-Limited Data in April 2018 that brought together 25 leading stakeholders selected among researchers, librarians, content providers, legal experts, and representatives of scholarly societies to articulate an agenda that provides…

How can we use user-generated content to construct, infer or refine network data? We have been tackling this problem by leveraging communication content produced and disseminated in social networks to enhance graph data. For example, we have used domain-adjusted sentiment analysis to label graphs with valence values in order to enable triadic balance assessment. The resulting method enables…

Assistant Professor Jana Diesner a received an Faculty Fellowship and seed funding for her project, “Predictive Modeling for Impact Assessment,” from the National Center for Supercomputing Applications (NCSA). Diesner collaborates closely with NCSA scientists on the project, which builds on her work developing computational solutions…

This project will develop a mobile sensor technology for performing detection and identification of viral and bacterial pathogens. By means of a smartphone-based detection instrument, the results are shared with a cloud-based data management service that will enable physicians to rapidly visualize the geographical and temporal spread of infectious disease. When deployed by a community of…

Recent research has demonstrated that investigations of contemporary societal problems can benefit from the use of long-term environmental data and from comparisons with cases in which the interactions of human societies with their environments is well-documented over centuries. By providing easy access to time- and place-specific long-term environmental data, this project seeks to facilitate…

Music prints and manuscripts created over the past thousand years sit on the shelves of libraries and museums around the globe. As these organizations digitize their collections, images of these scores are increasingly accessible online. However, the musical content remains difficult to search.

Google Books and HathiTrust have already made it possible to search the content of text…

Monitoring drinking water contamination is vitally important to inform consumers about water safety, identify source water problems, and facilitate discussion of public health and the environment of our drinking water. The overall goal of this project is to develop a framework for reliable and timely detection of drinking water contamination to build sustainable and connected communities. It…

This project will create both a master’s and doctoral-level specialization in Socio-technical Data Analytics (SODA). Partnerships with local researchers and businesses who already work with large data-sets will enable master's graduates to receive first-hand experience with both the social and technical implications of large digital data collections, and thus be well-prepared for leadership…

In this collaborative project, we are using Natural Language Processing and Machine Learning to identify secondary practical uses of research findings from final reports of grant funded work. Such reports are often stored in specialized databases, where long-term archiving activities focus on standardization, interoperability, and information indexing and retrieval. However, secondary use of…

Scholarly publications today are still mostly disconnected from the underlying data and code used to produce the published results and findings, despite an increasing recognition of the need to share all aspects of the research process. As data become more open and transportable, a second layer of research output has emerged, linking research publications to the associated data, possibly along…

Graph Neural Networks have proven to be a powerful tool for harnessing graph data, which is widely used for representing rich relational information in multiple areas. However, the performance of graph neural networks largely depends on the amount of labeled data, which is subject to an expensive and time-consuming annotation process. This creates data without labels, or a label scarcity.…

The yt project aims to produce an integrated science environment for collaboratively asking and answering astrophysical questions. To do so, it will encompass the creation of initial conditions, the execution of simulations, and the detailed exploration and visualization of the resultant data. It will also provide a standard framework based on physical quantities interoperability between codes…